Control over infrastructure was mentioned by 41% of IT leaders. The argument for greater control is not new, but it has gained renewed relevance when paired with cost optimization strategies. Simply put, enterprises are asking tough questions about whether the public cloud meets all their operational needs. For an increasing number of organizations, the answer is no.
AI spending on the cloud
Most AI deployments illustrate the challenges with public cloud costs. According to the Crayon Report, 60% of enterprises use AI to optimize IT process automation, while 45% deploy AI for predictive cost analytics. This move underscores how businesses are leaning on machine learning models to improve resource planning and forecasting. However, running AI workloads at scale in the cloud is expensive, especially for organizations that utilize large computational models or require GPUs for specialized tasks.
Public cloud providers such as AWS, Microsoft Azure, and Google Cloud have responded with AI-optimized services and product offerings, but those often come with hefty price tags. The synergy between AI and cloud has clearly driven massive innovation, but it has also made it harder to manage cloud spending effectively. This is why cloud optimization strategies that cut costs without sacrificing performance are now crucial for maintaining financial stability amid increasing technological complexity.
